NTIRE 2017 : CVPR 2017- New Trends in Image Restoration and Enhancement workshop and challenge on image super-resolution
Call For Papers
NTIRE: New Trends in Image Restoration and Enhancement workshop and challenge on image super-resolution 2017
In conjunction with CVPR 2017
Contact: radu.timofte [at] vision.ee.ethz.ch
Image restoration and image enhancement are key computer vision tasks, aiming at the restoration of degraded image content or the filling in of missing information. Recent years have witnessed an increased interest from the vision and graphics communities in these fundamental topics of research. Not only has there been a constantly growing flow of related papers, but also substantial progress has been achieved.
Each step forward eases the use of images by people or computers for the fulfillment of further tasks, with image restoration or enhancement serving as an important frontend. Not surprisingly then, there is an ever growing range of applications in fields such as surveillance, the automotive industry, electronics, remote sensing, or medical image analysis. The emergence and ubiquitous use of mobile and wearable devices offer another fertile ground for additional applications and faster methods.
This workshop aims to provide an overview of the new trends and advances in those areas. Moreover, it will offer an opportunity for academic and industrial attendees to interact and explore collaborations.
Papers addressing topics related to image restoration and enhancement are invited. The topics include, but are not limited to:
● Image inpainting
● Image deblurring
● Image denoising
● Image upsampling and super-resolution
● Image filtering
● Image dehazing
● Image enhancement: brightening, color adjustment, sharpening, etc.
● Image-quality assessment
● Video restoration and enhancement
● Hyperspectral imaging
● Methods robust to changing weather conditions
● Studies and applications of the above.
A paper submission has to be in English, in pdf format, and at most 8 pages (excluding references) in CVPR style. The paper format must follow the same guidelines as for all CVPR submissions.
The review process is double blind. Authors do not know the names of the chair/reviewers of their papers. Reviewers do not know the names of the authors.
Dual submission is allowed with CVPR main conference only. If a paper is submitted also to CVPR and accepted, the paper cannot be published both at the CVPR and the workshop.
For the paper submissions, please go to the online submission site.
Accepted and presented papers will be published after the conference in the CVPR Workshops Proceedings on by IEEE (http://www.ieee.org) and Computer Vision Foundation (www.cv-foundation.org).
The author kit provides a LaTeX2e template for paper submissions. Please refer to the example for detailed formatting instructions. If you use a different document processing system then see the CVPR author instruction page.
Author Kit: http://cvpr2017.thecvf.com/files/cvpr2017AuthorKit.zip
● Submission Deadline: April 17, 2017
● Decisions: May 08, 2017
● Camera Ready Deadline: May 18, 2017
Challenge on Example-based Single-Image Super-Resolution
In order to gauge the current state-of-the-art in example-based single-image super-resolution, to compare and to promote different solutions we are organizing an NTIRE challenge in conjunction with the CVPR 2017 conference. We propose a large DIV2K dataset with DIVerse 2K resolution images.
The challenge has 2 tracks:
● Track 1: bicubic uses the bicubic downscaling (Matlab imresize), one of the most common settings from the recent single-image super-resolution literature.
● Track 2: unknown assumes that the explicit forms for the degradation operators are unknown, only the training pairs of low and high images are available.
To learn more about the challenge, to participate in the challenge, and to access the newly collected DIV2K dataset with DIVerse 2K resolution images everybody is invited to register at the links from:
The training data is already made available to the registered participants.
● Release of train data: February 14, 2017
● Validation server online: February 25, 2017
● Competition ends: March 31, 2017
● Radu Timofte, ETH Zurich, Switzerland (radu.timofte [at] vision.ee.ethz.ch)
● Ming-Hsuan Yang, University of California at Merced, US (mhyang [at] ucmerced.edu)
● Eirikur Agustsson, ETH Zurich, Switzerland (eirikur.agustsson [at] vision.ee.ethz.ch)
● Lei Zhang, The Hong Kong Polytechnic University (cslzhang [at] polyu.edu.hk)
● Luc Van Gool, KU Leuven, Belgium and ETH Zurich, Switzerland (vangool [at] vision.ee.ethz.ch)
Cosmin Ancuti, Université catholique de Louvain (UCL), Belgium
Michael S. Brown, York University, Canada
Subhasis Chaudhuri, IIT Bombay, India
Sunghyun Cho, Samsung
Oliver Cossairt, Northwestern University, US
Chao Dong, SenseTime
Weisheng Dong, Xidian University, China
Alessandro Foi, Tampere University of Technology, Finland
Luc Van Gool, ETH Zürich and KU Leuven, Belgium
Peter Gehler, University of Tübingen and MPI Intelligent Systems, Germany
Hiroto Honda, Toshiba Co.
Michal Irani, Weizmann Institute, Israel
Zhe Hu, Light.co
Kyoung Mu Lee, Seoul National University, South Korea
Chen Change Loy, Chinese University of Hong Kong
Vladimir Lukin, National Aerospace University, Ukraine
Kai-Kuang Ma, Nanyang Technological University, Singapore
Vasile Manta, Technical University of Iasi, Romania
Yasuyuki Matsushita, Osaka University, Japan
Peyman Milanfar, Google and UCSC, US
Yusuke Monno, Tokyo Institute of Technology, Japan
Hajime Nagahara, Kyushu University, Japan
Vinay P. Namboodiri, IIT Kanpur, India
Aleksandra Pizurica, Ghent University, Belgium
Fatih Porikli, Australian National University, NICTA, Australia
Stefan Roth, TU Darmstadt, Germany
Aline Roumy, INRIA, France
Nicu Sebe, University of Trento, Italy
Boxin Shi, National Institute of Advanced Industrial Science and Technology (AIST), Japan
Sabine Süsstrunk, EPFL, Switzerland
Hugues Talbot, Université Paris Est, France
Yu-Wing Tai, SenseTime
Robby T. Tan, Yale-NUS College, Singapore
Masayuki Tanaka, Tokyo Institute of Technology, Japan
Radu Timofte, ETH Zürich, Switzerland
Chih-Yuan Yang, UC Merced, US
Ming-Hsuan Yang, University of California at Merced, US
Qingxiong Yang, Didi Chuxing, China
Lei Zhang, The Hong Kong Polytechnic University
Wangmeng Zuo, Harbin Institute of Technology, China
Email: radu.timofte [at] vision.ee.ethz.ch